Text Recognition in Low-Quality Images via Multi-Operator Processing
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Solution Overview
Problem
Optical Character Recognition (OCR) systems struggle with accurately recognizing text in low-quality images captured using low-resolution digital cameras or mobile phones, due to factors like intensity variations, poor focus, and relative motion, leading to misinterpretation and loss of adjacency information.
Innovation Solution
Applying different image processing operators to generate multiple versions of an image, extracting character and location information, and editing the text based on relative character positions to improve recognition, including techniques like concatenating or splitting words and inserting spaces, while searching for matches in a domain database to correct text interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If OCR systems process low-quality images captured by mobile phones or low-resolution cameras, then the system can handle more diverse image sources and improve accessibility, but text recognition accuracy deteriorates due to intensity variations, poor focus, and relative motion
Solution Approach 1:
The patent segments the text recognition process into multiple independent stages: image quality assessment, selective image processing operations, character detection, and post-processing correction. This segmentation allows the system to apply different processing strategies to different aspects of the recognition pipeline, improving overall accuracy without requiring all images to meet high quality standards
Solution Approach 2:
The patent dynamically changes processing parameters based on image quality assessment. Different image processing operators are applied selectively based on detected image characteristics, and correction rules are applied based on the specific errors identified in the recognition output, allowing the system to adapt to varying image qualities
2Productivity
If OCR systems rely solely on detecting text in images without semantic analysis, then the system complexity is reduced and processing speed is improved, but the system cannot correctly interpret stylized text in signs or assign meaning to detected text
Solution Approach 1:
The patent introduces an intermediary correction layer between character detection and final output. This correction layer applies context-aware rules to fix common recognition errors such as misplaced spaces, concatenated words, and missing characters, improving interpretation accuracy without requiring full semantic analysis of the image content
Solution Approach 2:
The patent performs preliminary correction of recognition errors based on common patterns before final output. By anticipating and correcting typical OCR errors in advance, the system improves accuracy without adding complex semantic analysis steps during the main processing flow
3Measurement precision
If multiple image processing operators are applied to generate multiple processed versions of an image, then text recognition accuracy is improved through better handling of poor quality images, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies partial processing by selecting and applying only the necessary image processing operators based on image quality assessment. Instead of applying all possible operators to all images, the system applies only those operators needed to correct the specific defects present in each image, reducing unnecessary processing time
Solution Approach 2:
The patent segments the image processing into multiple independent operator stages, allowing selective application of operators based on image quality assessment. This segmentation enables the system to apply only the necessary processing steps to each image, reducing overall processing time while maintaining accuracy
Data Source
AI summary
Various embodiments of the invention describe a method, system and computer-readable storage medium containing instructions for improving the recognition of text present in an image. The image is processed by applying different operators to the image to obtain multiple processed versions of the image. Thereafter, characters and location information of the characters from each of the multiple processed versions of the image are obtained. The location information includes the pixel coordinates of the characters in the text. The text present in the image is edited, based on the relative location of the characters, to improve the recognition of the text in the image.